Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms

The current paper presents a novel scheme for bearing fault diagnosis based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), support vector machines (SVMs), and genetic algorithms (GAs). In the proposed scheme, bearing vibration signals were first decomposed into different frequency sub-bands through a four-level LWPT, resulting in a total of 31 node signal components throughout all layers of the LWPT decomposition tree. The SampEns of all 31 components were then calculated as an original feature pool to characterize the complexity of the bearing vibration signals within the corresponding frequency bands. For selecting the most informative features thus reducing the number of features, a GA was applied to simultaneously pick out the salient features and optimize the parameters of SVMs so as to avoid the curse of dimensionality, alleviate computational burden, and improve the subsequent classification. Experiments were conducted on an induction motor with respect to various bearing faults and a range of fault severities. As an example here, three salient features were selected from the original 31 features, with the dimension of features reduced dramatically. The selected three features were then presented into the optimized SVM to identify various bearing conditions. The scheme was compared with the widely used WPT-Energy method and with a neural network classifier with respect to feature extractions and fault classifications, respectively. The results are in favour of the proposed scheme in terms of the feature dimension, number of support vectors, robustness to the data partition, and classification rate.